{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyTorch Deep Explainer MNIST example\n",
    "\n",
    "A simple example showing how to explain an MNIST CNN trained using PyTorch with Deep Explainer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.nn import functional as F\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.311112\n",
      "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 2.219999\n",
      "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 1.546236\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.834946\n",
      "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.731919\n",
      "\n",
      "Test set: Average loss: 0.0046, Accuracy: 9006/10000 (90%)\n",
      "\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.725186\n",
      "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.456231\n",
      "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.522802\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.553828\n",
      "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.332361\n",
      "\n",
      "Test set: Average loss: 0.0026, Accuracy: 9377/10000 (94%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "num_epochs = 2\n",
    "device = torch.device(\"cpu\")\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.conv_layers = nn.Sequential(\n",
    "            nn.Conv2d(1, 10, kernel_size=5),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(10, 20, kernel_size=5),\n",
    "            nn.Dropout(),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.fc_layers = nn.Sequential(\n",
    "            nn.Linear(320, 50),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(),\n",
    "            nn.Linear(50, 10),\n",
    "            nn.Softmax(dim=1),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv_layers(x)\n",
    "        x = x.view(-1, 320)\n",
    "        x = self.fc_layers(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def train(model, device, train_loader, optimizer, epoch):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output.log(), target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % 100 == 0:\n",
    "            print(\n",
    "                f\"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)}\"\n",
    "                f\" ({100.0 * batch_idx / len(train_loader):.0f}%)]\"\n",
    "                f\"\\tLoss: {loss.item():.6f}\"\n",
    "            )\n",
    "\n",
    "\n",
    "def test(model, device, test_loader):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += F.nll_loss(output.log(), target).item()  # sum up batch loss\n",
    "            pred = output.max(1, keepdim=True)[1]  # get the index of the max log-probability\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print(\n",
    "        f\"\\nTest set: Average loss: {test_loss:.4f},\"\n",
    "        f\" Accuracy: {correct}/{len(test_loader.dataset)}\"\n",
    "        f\" ({100.0 * correct / len(test_loader.dataset):.0f}%)\\n\"\n",
    "    )\n",
    "\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST(\n",
    "        \"mnist_data\",\n",
    "        train=True,\n",
    "        download=True,\n",
    "        transform=transforms.Compose([transforms.ToTensor()]),\n",
    "    ),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST(\"mnist_data\", train=False, transform=transforms.Compose([transforms.ToTensor()])),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "model = Net().to(device)\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n",
    "\n",
    "for epoch in range(1, num_epochs + 1):\n",
    "    train(model, device, train_loader, optimizer, epoch)\n",
    "    test(model, device, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-16 23:59:57.692986: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-05-16 23:59:58.335002: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "# since shuffle=True, this is a random sample of test data\n",
    "batch = next(iter(test_loader))\n",
    "images, _ = batch\n",
    "\n",
    "background = images[:100]\n",
    "test_images = images[100:103]\n",
    "\n",
    "e = shap.DeepExplainer(model, background)\n",
    "shap_values = e.shap_values(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_numpy = list(np.transpose(shap_values, (4, 0, 2, 3, 1)))\n",
    "test_numpy = np.swapaxes(np.swapaxes(test_images.numpy(), 1, -1), 1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x606.061 with 34 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the feature attributions\n",
    "shap.image_plot(shap_numpy, -test_numpy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the explanations for each class on four predictions. Note that the explanations are ordered for the classes 0-9 going left to right along the rows."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
